library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(TH.data)
library(psych)
library(whitening)
library("vioplot")
library("rpart")
library(mlbench)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Source W. Nick Street, Olvi L. Mangasarian and William H. Wolberg (1995). An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522–530, San Francisco, Morgan Kaufmann.
Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.
wpbc {TH.data}
data("wpbc", package = "TH.data")
table(wpbc[,"status"])
#>
#> N R
#> 151 47
sum(1*(wpbc[,"status"]=="R" & wpbc$time <= 24))
#> [1] 29
wpbc <- subset(wpbc,time > 36 | status=="R" )
summary(wpbc$time)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.00 36.75 60.50 58.79 78.75 125.00
wpbc[,"status"] <- 1*(wpbc[,"status"]=="R")
wpbc <- wpbc[complete.cases(wpbc),]
pander::pander(table(wpbc[,"status"]))
| 0 | 1 |
|---|---|
| 91 | 46 |
wpbc$time <- NULL
studyName <- "Wisconsin"
dataframe <- wpbc
outcome <- "status"
thro <- 0.4
TopVariables <- 10
cexheat = 0.25
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 137 | 32 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 91 | 46 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9961379
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 32 , Uni p: 0.0078125 , Outcome-Driven Size: 0 , Base Size: 4 , Rcrit: 0.2062105
#>
#>
1 <R=0.996,thr=0.900,N= 9>, Top: 2( 5 )[ 1 : 2 Fa= 2 : 0.900 ]( 2 , 7 , 0 ),<|>Tot Used: 9 , Added: 7 , Zero Std: 0 , Max Cor: 0.978
#>
2 <R=0.978,thr=0.900,N= 9>, Top: 1( 2 )[ 1 : 1 Fa= 3 : 0.900 ]( 1 , 2 , 2 ),<|>Tot Used: 9 , Added: 2 , Zero Std: 0 , Max Cor: 0.891
#>
3 <R=0.891,thr=0.750,N= 20>, Top: 5( 2 )[ 1 : 5 Fa= 8 : 0.750 ]( 5 , 9 , 3 ),<|>Tot Used: 20 , Added: 9 , Zero Std: 0 , Max Cor: 0.783
#>
4 <R=0.783,thr=0.750,N= 20>, Top: 2( 1 )[ 1 : 2 Fa= 10 : 0.750 ]( 2 , 2 , 8 ),<|>Tot Used: 24 , Added: 2 , Zero Std: 0 , Max Cor: 0.744
#>
5 <R=0.744,thr=0.600,N= 14>, Top: 3( 6 )[ 1 : 3 Fa= 10 : 0.600 ]( 3 , 9 , 10 ),<|>Tot Used: 26 , Added: 9 , Zero Std: 0 , Max Cor: 0.792
#>
6 <R=0.792,thr=0.750,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 10 : 0.750 ]( 1 , 1 , 10 ),<|>Tot Used: 26 , Added: 1 , Zero Std: 0 , Max Cor: 0.727
#>
7 <R=0.727,thr=0.600,N= 11>, Top: 5( 1 )[ 1 : 5 Fa= 14 : 0.600 ]( 5 , 6 , 10 ),<|>Tot Used: 28 , Added: 6 , Zero Std: 0 , Max Cor: 0.789
#>
8 <R=0.789,thr=0.750,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 15 : 0.750 ]( 1 , 1 , 14 ),<|>Tot Used: 28 , Added: 1 , Zero Std: 0 , Max Cor: 0.699
#>
9 <R=0.699,thr=0.600,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 16 : 0.600 ]( 2 , 2 , 15 ),<|>Tot Used: 28 , Added: 2 , Zero Std: 0 , Max Cor: 0.591
#>
10 <R=0.591,thr=0.450,N= 23>, Top: 8( 5 )[ 1 : 8 Fa= 17 : 0.450 ]( 8 , 14 , 16 ),<|>Tot Used: 31 , Added: 14 , Zero Std: 0 , Max Cor: 0.567
#>
11 <R=0.567,thr=0.450,N= 23>, Top: 6( 1 )[ 1 : 6 Fa= 18 : 0.450 ]( 6 , 7 , 17 ),<|>Tot Used: 32 , Added: 7 , Zero Std: 0 , Max Cor: 0.586
#>
12 <R=0.586,thr=0.450,N= 23>, Top: 3( 1 )[ 1 : 3 Fa= 19 : 0.450 ]( 3 , 3 , 18 ),<|>Tot Used: 32 , Added: 3 , Zero Std: 0 , Max Cor: 0.654
#>
13 <R=0.654,thr=0.600,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 20 : 0.600 ]( 1 , 1 , 19 ),<|>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.445
#>
14 <R=0.445,thr=0.400,N= 11>, Top: 4( 2 )[ 1 : 4 Fa= 20 : 0.400 ]( 4 , 6 , 20 ),<|>Tot Used: 32 , Added: 6 , Zero Std: 0 , Max Cor: 0.558
#>
15 <R=0.558,thr=0.450,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 20 : 0.450 ]( 2 , 2 , 20 ),<|>Tot Used: 32 , Added: 2 , Zero Std: 0 , Max Cor: 0.393
#>
16 <R=0.393,thr=0.400,N= 0>
#>
[ 16 ], 0.3929034 Decor Dimension: 32 Nused: 32 . Cor to Base: 26 , ABase: 4 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
515156
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
4500
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
1.39
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
1.49
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPLTM <- attr(DEdataframe,"UPLTM")
gplots::heatmap.2(1.0*(abs(UPLTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.3929034
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| tsize | 3.47 | 2.03 | 2.64 | 1.86 | 1.11e-03 | 0.666 |
| pnodes | 4.87 | 6.02 | 2.63 | 5.21 | 6.25e-09 | 0.650 |
| worst_radius | 22.67 | 4.70 | 20.35 | 4.08 | 3.68e-01 | 0.647 |
| worst_perimeter | 151.33 | 32.42 | 135.34 | 26.85 | 5.71e-01 | 0.645 |
| mean_area | 1081.98 | 397.26 | 888.40 | 310.85 | 1.26e-01 | 0.645 |
| worst_area | 1635.77 | 703.15 | 1317.95 | 550.94 | 2.72e-01 | 0.643 |
| mean_perimeter | 121.10 | 22.91 | 110.02 | 19.19 | 4.72e-01 | 0.641 |
| mean_radius | 18.33 | 3.37 | 16.70 | 2.91 | 3.12e-01 | 0.639 |
| SE_perimeter | 4.73 | 2.21 | 3.81 | 1.80 | 6.37e-02 | 0.634 |
| SE_area | 81.97 | 53.36 | 61.22 | 37.72 | 6.46e-02 | 0.632 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| tsize | 3.47e+00 | 2.02985 | 2.63846 | 1.85507 | 0.001110 | 0.666 |
| worst_perimeter | 1.51e+02 | 32.41729 | 135.33967 | 26.84680 | 0.570824 | 0.645 |
| La_mean_symmetry | 1.35e-01 | 0.02133 | 0.14245 | 0.02170 | 0.100217 | 0.605 |
| La_SE_area | 4.71e+01 | 10.66115 | 48.87774 | 7.60123 | 0.330672 | 0.603 |
| La_worst_area | -6.04e+01 | 31.62288 | -51.08648 | 48.03744 | 0.106608 | 0.570 |
| La_worst_concavity | 1.80e-01 | 0.05779 | 0.16183 | 0.06191 | 0.996291 | 0.568 |
| La_SE_symmetry | -7.92e-03 | 0.00840 | -0.00873 | 0.00969 | 0.000342 | 0.563 |
| La_worst_compactness | 9.05e-02 | 0.05599 | 0.10973 | 0.06754 | 0.270576 | 0.561 |
| La_SE_fractaldim | 9.68e-04 | 0.00106 | 0.00119 | 0.00103 | 0.045469 | 0.559 |
| La_mean_perimeter | -5.83e+00 | 0.88280 | -6.01118 | 0.87043 | 0.647736 | 0.553 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 4.52 | 27 | 0.844 |
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| tsize | NA | 3.47e+00 | 2.03e+00 | 2.64e+00 | 1.86e+00 | 1.11e-03 | 0.666 | 0.666 | 1 |
| tsize1 | NA | 3.47e+00 | 2.03e+00 | 2.64e+00 | 1.86e+00 | 1.11e-03 | 0.666 | NA | NA |
| pnodes | NA | 4.87e+00 | 6.02e+00 | 2.63e+00 | 5.21e+00 | 6.25e-09 | 0.650 | 0.650 | NA |
| worst_radius | NA | 2.27e+01 | 4.70e+00 | 2.03e+01 | 4.08e+00 | 3.68e-01 | 0.647 | 0.647 | NA |
| worst_perimeter | NA | 1.51e+02 | 3.24e+01 | 1.35e+02 | 2.68e+01 | 5.71e-01 | 0.645 | 0.645 | 10 |
| worst_perimeter1 | NA | 1.51e+02 | 3.24e+01 | 1.35e+02 | 2.68e+01 | 5.71e-01 | 0.645 | NA | NA |
| mean_area | NA | 1.08e+03 | 3.97e+02 | 8.88e+02 | 3.11e+02 | 1.26e-01 | 0.645 | 0.645 | NA |
| worst_area | NA | 1.64e+03 | 7.03e+02 | 1.32e+03 | 5.51e+02 | 2.72e-01 | 0.643 | 0.643 | NA |
| mean_perimeter | NA | 1.21e+02 | 2.29e+01 | 1.10e+02 | 1.92e+01 | 4.72e-01 | 0.641 | 0.641 | NA |
| mean_radius | NA | 1.83e+01 | 3.37e+00 | 1.67e+01 | 2.91e+00 | 3.12e-01 | 0.639 | 0.639 | NA |
| SE_perimeter | NA | 4.73e+00 | 2.21e+00 | 3.81e+00 | 1.80e+00 | 6.37e-02 | 0.634 | 0.634 | NA |
| SE_area | NA | 8.20e+01 | 5.34e+01 | 6.12e+01 | 3.77e+01 | 6.46e-02 | 0.632 | 0.632 | NA |
| La_mean_symmetry | - (0.372)mean_compactness + mean_symmetry | 1.35e-01 | 2.13e-02 | 1.42e-01 | 2.17e-02 | 1.00e-01 | 0.605 | 0.580 | 1 |
| La_SE_area | + (11.643)mean_radius - (0.119)mean_area - (1.18e+02)SE_radius + SE_area - (0.270)worst_perimeter | 4.71e+01 | 1.07e+01 | 4.89e+01 | 7.60e+00 | 3.31e-01 | 0.603 | 0.632 | -3 |
| La_worst_area | + (1.37e+02)mean_radius - (1.142)mean_area + (2.92e+02)SE_radius + (31.175)SE_perimeter - (4.282)SE_area - (1.20e+02)worst_radius - (1.607)worst_perimeter + worst_area | -6.04e+01 | 3.16e+01 | -5.11e+01 | 4.80e+01 | 1.07e-01 | 0.570 | 0.643 | -6 |
| La_worst_concavity | + (0.803)mean_compactness - (2.781)mean_concavity + (3.387)mean_concavepoints - (0.672)worst_compactness + worst_concavity | 1.80e-01 | 5.78e-02 | 1.62e-01 | 6.19e-02 | 9.96e-01 | 0.568 | 0.492 | -3 |
| La_SE_symmetry | + (0.077)mean_compactness - (0.206)mean_symmetry + SE_symmetry | -7.92e-03 | 8.40e-03 | -8.73e-03 | 9.69e-03 | 3.42e-04 | 0.563 | 0.504 | 0 |
| La_worst_compactness | + (0.027)mean_radius - (1.559)mean_compactness + (0.215)SE_radius + (8.953)SE_smoothness - (4.338)SE_compactness - (4.01e-03)worst_perimeter + worst_compactness | 9.05e-02 | 5.60e-02 | 1.10e-01 | 6.75e-02 | 2.71e-01 | 0.561 | 0.538 | 3 |
| La_SE_fractaldim | - (0.093)SE_compactness + SE_fractaldim | 9.68e-04 | 1.06e-03 | 1.19e-03 | 1.03e-03 | 4.55e-02 | 0.559 | 0.525 | 2 |
| La_mean_perimeter | - (6.478)mean_radius + mean_perimeter - (28.329)mean_compactness - (0.028)worst_perimeter | -5.83e+00 | 8.83e-01 | -6.01e+00 | 8.70e-01 | 6.48e-01 | 0.553 | 0.641 | -2 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 68 | 23 |
| 1 | 9 | 37 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.766 | 0.687 | 0.834 |
| 3 | se | 0.804 | 0.661 | 0.906 |
| 4 | sp | 0.747 | 0.645 | 0.833 |
| 6 | diag.or | 12.155 | 5.100 | 28.966 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 69 | 22 |
| 1 | 13 | 33 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.745 | 0.663 | 0.815 |
| 3 | se | 0.717 | 0.565 | 0.840 |
| 4 | sp | 0.758 | 0.657 | 0.842 |
| 6 | diag.or | 7.962 | 3.572 | 17.743 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 86 | 5 |
| 1 | 27 | 19 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.766 | 0.687 | 0.834 |
| 3 | se | 0.413 | 0.270 | 0.568 |
| 4 | sp | 0.945 | 0.876 | 0.982 |
| 6 | diag.or | 12.104 | 4.128 | 35.493 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 90 | 1 |
| 1 | 38 | 8 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.715 | 0.6320 | 0.789 |
| 3 | se | 0.174 | 0.0782 | 0.314 |
| 4 | sp | 0.989 | 0.9403 | 1.000 |
| 6 | diag.or | 18.947 | 2.2899 | 156.776 |
par(op)